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NVIDIA AI Infrastructure Sample Questions (Q183-Q188):

NEW QUESTION # 183
You're setting up a BlueField-3 DPIJ to offload storage virtualization tasks. Specifically, you want to use SPDK (Storage Performance Development Kit) on the DPIJ. What are the MINIMUM required steps to enable SPDK on the BlueField-3 after the DPIJ has been flashed with the appropriate OS image? (Select TWO)

A. Configure the Huge Pages settings in the DPU's kernel to allocate sufficient memory for SPDK.
B. Download and compile the SPDK source code directly on the DPU.
C. Install the SPDK packages using the DPU's package manager (e.g., 'apt install spdk').
D. Configure the network interfaces on the DPIJ to support RDMA or NVMe-oF, depending on the desired storage protocol.
E. Enable the SPDK service using 'systemctl enable spdK and 'systemctl start spdk'.

Answer: A,C

Explanation:
The minimum steps involve installing the SPDK packages using the DPU's package manager, assuming prebuilt packages are available, and configuring Huge Pages. SPDK relies heavily on Huge Pages for memory management. While configuring network interfaces and starting services are important, installing SPDK and configuring Huge Pages are required first steps. Downloading and compiling from source might be necessary in some cases but not minimally required if packages are available.

NEW QUESTION # 184
Which of the following is the MOST important reason for using a dedicated storage network (e.g., InfiniBand or RoCE) for AI/ML workloads compared to using the existing Ethernet network?

A. Improved security due to network isolation.
B. Simplified network management and configuration.
C. Lower latency and higher bandwidth for data transfer.
D. Automatic Quality of Service (QOS) prioritization for AI/ML traffic.
E. Reduced cost compared to upgrading the existing Ethernet infrastructure.

Answer: C

Explanation:
The primary benefit of a dedicated storage network like InfiniBand or RoCE is the significant improvement in latency and bandwidth (option B) compared to Ethernet. These technologies are designed for high-performance computing and can handle the intense 1/0 demands of AI/ML workloads. While security (A) can be improved through isolation, and QOS (E) is possible, the performance advantage is the most crucial factor. Cost (D) is generally higher, and management (C) can be more complex.

NEW QUESTION # 185
You are deploying a security application that leverages the BlueField DPIJ to perform deep packet inspection (DPI) on network traffic.
Your application requires access to the raw packet data, including the Ethernet headers. Which of the following programming models or APIs is most suitable for accessing raw packet data on the BlueField DPUwith minimal overhead?

A. Using libpcap to capture packets from the network interface.
B. Using the 'tcpdump' command and parsing its output.
C. Using Netfilter hooks in the Linux kernel to intercept packets.
D. Using standard Linux socket APIs (e.g., 'socket()' , 'recvfrom()') in user space.
E. using DPDK (Data Plane Development Kit) with the BlueField DPU's PMD (Poll Mode Driver).

Answer: E

Explanation:
DPDK provides a framework for high-performance packet processing in user space, bypassing the kernel's networking stack. The BlueField DPU's PMD (Poll Mode Driver) allows DPDK applications to directly access the network interface with minimal overhead. This is ideal for DPI applications that need to process a large volume of packets at high speed. Standard socket APIs and Netfilter hooks involve kernel intervention, which can introduce significant overhead. Libpcap is a packet capture library, not designed for high-performance packet processing. 'tcpdump' is a command-line tool, not a programming API.

NEW QUESTION # 186
A user reports that their deep learning training job is crashing with a 'CUDA out of memory' error, even though 'nvidia-smi' shows plenty of free memory on the GPU. The job uses TensorFlow. What are the TWO most likely causes?

A. The CUDA VISIBLE DEVICES environment variable is not set correctly.
B. The TensorFlow version is incompatible with the installed NVIDIA driver.
C. TensorFlow is allocating memory on the CPU instead of the GPU.
D. The system's swap space is full, preventing memory from being allocated.
E. TensorFlow is fragmenting GPU memory, making it difficult to allocate contiguous blocks.

Answer: A,E

Explanation:
'CUDA out of memory errors, despite seemingly available GPU memory, often indicate memory fragmentation or improper GPU assignment. TensorFlow can fragment GPU memory, leading to allocation failures even if sufficient total memory is available. The variable controls which GPUs TensorFlow can access. If it's not set or is set incorrectly, TensorFlow might be trying to allocate memory on a non-existent or unavailable GPU. While TensorFlow version incompatibilities can cause issues, they are less likely to directly manifest as 'CUDA out of memory' errors. TensorFlow typically prioritizes GPU memory allocation if configured correctly.

NEW QUESTION # 187
You are experiencing link flapping (frequent up/down transitions) on several InfiniBand links in your AI infrastructure. This is causing intermittent connectivity issues and performance degradation. What are the MOST likely causes of this issue, and what steps should you take to troubleshoot and resolve it? (Select TWO)

A. Excessive broadcast traffic causing congestion.
B. Software bugs in the operating system or InfiniBand drivers.
C. Incorrect MTU (Maximum Transmission Unit) configuration on the affected interfaces.
D. Faulty or damaged cables, connectors, or transceivers.
E. Mismatched link speeds or duplex settings between connected devices.

Answer: D,E

Explanation:
Link flapping is most commonly caused by physical layer issues (faulty cables, connectors, or transceivers) or configuration mismatches (link speeds or duplex settings). Troubleshooting should focus on inspecting the physical connections and verifying that the link speed and duplex settings are correctly configured on both ends of the link. While MTIJ issues and software bugs can cause network problems, they are less likely to directly cause link flapping. Excessive broadcast traffic can cause performance issues but is less likely to result in frequent link up/down transitions.

NEW QUESTION # 188
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